000 03323nam a22005295i 4500
001 978-3-662-48395-4
003 DE-He213
005 20200420220223.0
007 cr nn 008mamaa
008 160504s2016 gw | s |||| 0|eng d
020 _a9783662483954
_9978-3-662-48395-4
024 7 _a10.1007/978-3-662-48395-4
_2doi
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aCOM031000
_2bisacsh
082 0 4 _a004.0151
_223
245 1 0 _aTopics in Grammatical Inference
_h[electronic resource] /
_cedited by Jeffrey Heinz, Jos�e M. Sempere.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2016.
300 _aXVII, 247 p. 56 illus., 7 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Gold-Style Learning Theory -- Efficiency in the Identification in the Limit Learning Paradigm -- Learning Grammars and Automata with Queries -- On the Inference of Finite State Automata from Positive and Negative Data -- Learning Probability Distributions Generated by Finite-State Machines -- Distributional Learning of Context-Free and Multiple -- Context-Free Grammars -- Learning Tree Languages -- Learning the Language of Biological Sequences.
520 _aThis book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book.
650 0 _aComputer science.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aBioinformatics.
650 0 _aComputational linguistics.
650 1 4 _aComputer Science.
650 2 4 _aTheory of Computation.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Linguistics.
650 2 4 _aComputational Biology/Bioinformatics.
700 1 _aHeinz, Jeffrey.
_eeditor.
700 1 _aSempere, Jos�e M.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783662483930
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-662-48395-4
912 _aZDB-2-SCS
942 _cEBK
999 _c52043
_d52043